"""
增强研究工具集

集成新的分析工具到现有工具链，包括：
- 智能文献分析工具
- 实验设计优化工具
- 数据处理和可视化工具
- 协作和通信工具
- 质量评估工具
"""
import json
import asyncio
import numpy as np
import pandas as pd
from typing import Dict, List, Any, Optional, Tuple
from datetime import datetime, timedelta
from dataclasses import dataclass, field

# 导入现有工具
from .common import HFDataSearch, SemanticScholarSearch, ArxivSearch, execute_code

# 导入研究自动化服务
try:
    from research_automation.services.agent_communication_service import (
        AgentCommunicationService, AgentMessage, MessageType, MessagePriority
    )
    from research_automation.services.intelligent_message_router import IntelligentMessageRouter
    from research_automation.services.agent_communication_integrator import AgentCommunicationIntegrator
except ImportError:
    # 如果导入失败，创建占位符类
    class AgentCommunicationService:
        pass
    class IntelligentMessageRouter:
        pass
    class AgentCommunicationIntegrator:
        pass


@dataclass
class ResearchContext:
    """研究上下文"""
    topic: str = ""
    phase: str = ""
    objectives: List[str] = field(default_factory=list)
    constraints: Dict[str, Any] = field(default_factory=dict)
    resources: Dict[str, Any] = field(default_factory=dict)
    timeline: Dict[str, Any] = field(default_factory=dict)


class EnhancedLiteratureAnalyzer:
    """增强文献分析器"""
    
    def __init__(self):
        self.arxiv_search = ArxivSearch()
        self.semantic_search = SemanticScholarSearch()
        self.analysis_cache = {}
    
    def analyze_literature_with_context(self, query: str, context: ResearchContext, 
                                      max_papers: int = 20) -> Dict[str, Any]:
        """基于上下文的智能文献分析"""
        try:
            # 生成上下文感知的搜索查询
            enhanced_query = self._enhance_query_with_context(query, context)
            
            # 搜索文献
            arxiv_results = self.arxiv_search.find_papers_by_str(enhanced_query, max_papers)
            semantic_results = self.semantic_search.find_papers_by_str(enhanced_query, max_papers)
            
            # 分析和整合结果
            analysis = {
                'query': enhanced_query,
                'arxiv_papers': self._parse_arxiv_results(arxiv_results),
                'semantic_papers': semantic_results,
                'research_gaps': self._identify_research_gaps(arxiv_results, context),
                'trending_topics': self._extract_trending_topics(arxiv_results),
                'methodology_insights': self._analyze_methodologies(arxiv_results),
                'citation_network': self._build_citation_network(semantic_results),
                'recommendations': self._generate_research_recommendations(context, arxiv_results)
            }
            
            return analysis
            
        except Exception as e:
            return {'error': f'文献分析失败: {str(e)}'}
    
    def _enhance_query_with_context(self, query: str, context: ResearchContext) -> str:
        """基于上下文增强搜索查询"""
        enhanced_parts = [query]
        
        # 添加研究阶段相关关键词
        phase_keywords = {
            'literature review': ['survey', 'review', 'state-of-the-art'],
            'methodology': ['method', 'approach', 'algorithm', 'framework'],
            'experiment': ['experiment', 'evaluation', 'benchmark', 'dataset'],
            'analysis': ['analysis', 'results', 'performance', 'comparison']
        }
        
        if context.phase in phase_keywords:
            enhanced_parts.extend(phase_keywords[context.phase])
        
        # 添加目标相关关键词
        for objective in context.objectives[:2]:  # 限制前2个目标
            enhanced_parts.append(objective.split()[:3])  # 取前3个词
        
        return ' '.join(str(part) for part in enhanced_parts if part)
    
    def _parse_arxiv_results(self, results: str) -> List[Dict[str, Any]]:
        """解析ArXiv搜索结果"""
        if not results:
            return []
        
        papers = []
        paper_blocks = results.split('\n\n')
        
        for block in paper_blocks:
            if not block.strip():
                continue
                
            paper = {}
            lines = block.strip().split('\n')
            
            for line in lines:
                if line.startswith('Title: '):
                    paper['title'] = line[7:]
                elif line.startswith('Summary: '):
                    paper['summary'] = line[9:]
                elif line.startswith('Publication Date: '):
                    paper['publication_date'] = line[18:]
                elif line.startswith('arXiv paper ID: '):
                    paper['arxiv_id'] = line[16:]
            
            if paper:
                papers.append(paper)
        
        return papers    

    def _identify_research_gaps(self, results: str, context: ResearchContext) -> List[str]:
        """识别研究空白"""
        gaps = []
        
        # 基于关键词频率分析识别空白
        if results:
            # 简化的空白识别逻辑
            common_topics = ['deep learning', 'machine learning', 'neural network']
            mentioned_topics = set()
            
            for topic in common_topics:
                if topic.lower() in results.lower():
                    mentioned_topics.add(topic)
            
            # 识别未充分探索的领域
            for objective in context.objectives:
                if not any(topic in objective.lower() for topic in mentioned_topics):
                    gaps.append(f"缺乏关于'{objective}'的深入研究")
        
        return gaps
    
    def _extract_trending_topics(self, results: str) -> List[str]:
        """提取热门话题"""
        if not results:
            return []
        
        # 简化的热门话题提取
        keywords = ['transformer', 'attention', 'bert', 'gpt', 'diffusion', 'gan', 'vae']
        trending = []
        
        for keyword in keywords:
            if keyword.lower() in results.lower():
                trending.append(keyword.upper())
        
        return trending
    
    def _analyze_methodologies(self, results: str) -> Dict[str, Any]:
        """分析方法论"""
        methodologies = {
            'supervised_learning': 0,
            'unsupervised_learning': 0,
            'reinforcement_learning': 0,
            'deep_learning': 0,
            'traditional_ml': 0
        }
        
        if results:
            text_lower = results.lower()
            
            # 统计不同方法论的出现频率
            if 'supervised' in text_lower:
                methodologies['supervised_learning'] += text_lower.count('supervised')
            if 'unsupervised' in text_lower:
                methodologies['unsupervised_learning'] += text_lower.count('unsupervised')
            if 'reinforcement' in text_lower:
                methodologies['reinforcement_learning'] += text_lower.count('reinforcement')
            if 'deep learning' in text_lower or 'neural network' in text_lower:
                methodologies['deep_learning'] += text_lower.count('deep learning') + text_lower.count('neural network')
        
        return methodologies
    
    def _build_citation_network(self, semantic_results: List[str]) -> Dict[str, Any]:
        """构建引用网络"""
        network = {
            'nodes': [],
            'edges': [],
            'metrics': {
                'total_papers': len(semantic_results),
                'avg_citations': 0,
                'highly_cited_threshold': 100
            }
        }
        
        total_citations = 0
        highly_cited_count = 0
        
        for i, paper in enumerate(semantic_results):
            # 简化的引用信息提取
            citations = 0
            if 'Citations:' in paper:
                try:
                    citations = int(paper.split('Citations: ')[1].split('\n')[0])
                    total_citations += citations
                    if citations > 100:
                        highly_cited_count += 1
                except:
                    pass
            
            network['nodes'].append({
                'id': i,
                'citations': citations,
                'highly_cited': citations > 100
            })
        
        if semantic_results:
            network['metrics']['avg_citations'] = total_citations / len(semantic_results)
            network['metrics']['highly_cited_count'] = highly_cited_count
        
        return network
    
    def _generate_research_recommendations(self, context: ResearchContext, 
                                         results: str) -> List[str]:
        """生成研究建议"""
        recommendations = []
        
        # 基于研究阶段的建议
        if context.phase == 'literature review':
            recommendations.append("建议扩展搜索范围，包含更多相关领域的文献")
            recommendations.append("关注最近2年的研究进展")
        elif context.phase == 'methodology':
            recommendations.append("比较不同方法的优缺点")
            recommendations.append("考虑方法的可复现性和计算复杂度")
        elif context.phase == 'experiment':
            recommendations.append("设计对照实验验证假设")
            recommendations.append("选择合适的评估指标和基准数据集")
        
        # 基于文献分析的建议
        if results and 'deep learning' in results.lower():
            recommendations.append("考虑深度学习方法的可解释性问题")
        
        return recommendations
class Sm
artExperimentDesigner:
    """智能实验设计器"""
    
    def __init__(self):
        self.design_templates = self._load_design_templates()
        self.optimization_history = []
    
    def design_experiment(self, research_context: ResearchContext, 
                         literature_analysis: Dict[str, Any]) -> Dict[str, Any]:
        """设计实验方案"""
        try:
            # 分析研究需求
            requirements = self._analyze_requirements(research_context, literature_analysis)
            
            # 选择实验设计模板
            template = self._select_design_template(requirements)
            
            # 生成实验方案
            experiment_design = {
                'experiment_id': f"exp_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
                'research_question': research_context.topic,
                'hypotheses': self._generate_hypotheses(research_context, literature_analysis),
                'methodology': self._design_methodology(template, requirements),
                'data_requirements': self._specify_data_requirements(requirements),
                'evaluation_metrics': self._select_evaluation_metrics(requirements),
                'experimental_setup': self._design_experimental_setup(template, requirements),
                'resource_estimation': self._estimate_resources(requirements),
                'timeline': self._create_experiment_timeline(requirements),
                'risk_assessment': self._assess_risks(requirements),
                'quality_controls': self._design_quality_controls(requirements)
            }
            
            return experiment_design
            
        except Exception as e:
            return {'error': f'实验设计失败: {str(e)}'}
    
    def _load_design_templates(self) -> Dict[str, Any]:
        """加载实验设计模板"""
        return {
            'supervised_learning': {
                'phases': ['data_preparation', 'model_training', 'evaluation', 'analysis'],
                'required_components': ['dataset', 'model', 'loss_function', 'optimizer'],
                'evaluation_methods': ['cross_validation', 'holdout', 'bootstrap']
            },
            'unsupervised_learning': {
                'phases': ['data_preparation', 'model_training', 'clustering_analysis', 'validation'],
                'required_components': ['dataset', 'model', 'similarity_metric'],
                'evaluation_methods': ['silhouette_analysis', 'elbow_method', 'gap_statistic']
            },
            'comparative_study': {
                'phases': ['baseline_setup', 'method_implementation', 'comparison', 'analysis'],
                'required_components': ['baseline_methods', 'proposed_method', 'evaluation_framework'],
                'evaluation_methods': ['statistical_testing', 'effect_size_analysis']
            }
        }
    
    def _analyze_requirements(self, context: ResearchContext, 
                            literature_analysis: Dict[str, Any]) -> Dict[str, Any]:
        """分析实验需求"""
        requirements = {
            'experiment_type': 'supervised_learning',  # 默认值
            'complexity_level': 'medium',
            'data_size': 'medium',
            'computational_requirements': 'medium',
            'novelty_level': 'incremental'
        }
        
        # 基于研究上下文分析
        if 'classification' in context.topic.lower() or 'prediction' in context.topic.lower():
            requirements['experiment_type'] = 'supervised_learning'
        elif 'clustering' in context.topic.lower() or 'dimensionality' in context.topic.lower():
            requirements['experiment_type'] = 'unsupervised_learning'
        elif 'comparison' in context.topic.lower() or 'benchmark' in context.topic.lower():
            requirements['experiment_type'] = 'comparative_study'
        
        # 基于文献分析调整需求
        if literature_analysis.get('methodology_insights', {}).get('deep_learning', 0) > 5:
            requirements['complexity_level'] = 'high'
            requirements['computational_requirements'] = 'high'
        
        return requirements
    
    def _select_design_template(self, requirements: Dict[str, Any]) -> Dict[str, Any]:
        """选择实验设计模板"""
        experiment_type = requirements.get('experiment_type', 'supervised_learning')
        return self.design_templates.get(experiment_type, self.design_templates['supervised_learning'])
    
    def _generate_hypotheses(self, context: ResearchContext, 
                           literature_analysis: Dict[str, Any]) -> List[str]:
        """生成研究假设"""
        hypotheses = []
        
        # 基于研究目标生成假设
        for objective in context.objectives:
            if 'improve' in objective.lower():
                hypotheses.append(f"所提出的方法在{objective}方面优于现有基准方法")
            elif 'analyze' in objective.lower():
                hypotheses.append(f"通过{objective}可以发现新的模式或规律")
        
        # 基于文献分析生成假设
        if literature_analysis.get('research_gaps'):
            for gap in literature_analysis['research_gaps'][:2]:
                hypotheses.append(f"针对{gap}的新方法将显著提升性能")
        
        return hypotheses